STY MAINT typing

README.md

@@ -192,7 +192,7 @@ $ pip install pandas_ta[full]
 
 Latest Version
 --------------
-Best choice! Version: *0.3.51b*
+Best choice! Version: *0.3.52b*
 * Includes all fixes and updates between **pypi** and what is covered in this README.
 ```sh
 $ pip install -U git+https://github.com/twopirllc/pandas-ta
@@ -1229,8 +1229,12 @@ Back to [Contents](#contents)
 <br/>
 
 # **Sources**
+### Technical Analysis
 [Original TA-LIB](http://ta-lib.org/) | [TradingView](http://www.tradingview.com) | [Sierra Chart](https://search.sierrachart.com/?Query=indicators&submitted=true) | [MQL5](https://www.mql5.com) | [FM Labs](https://www.fmlabs.com/reference/default.htm) | [Pro Real Code](https://www.prorealcode.com/prorealtime-indicators) | [User 42](https://user42.tuxfamily.org/chart/manual/index.html) | [Technical Traders](http://technical.traders.com/tradersonline/FeedTT-2014.html)
 
+### Supplemental
+[What Every Computer Scientist Should Know About Floating-Point Arithmetic](https://docs.oracle.com/cd/E19957-01/806-3568/ncg_goldberg.html)
+
 <br/>
 
 # **Support**

pandas_ta/_typing.py

@@ -0,0 +1,62 @@
+# -*- coding: utf-8 -*-
+from decimal import Decimal
+from functools import partial
+from pathlib import Path
+from typing import *
+
+from numpy import argmax, argmin, nan, ndarray, recarray, void
+from numpy import bool_ as np_bool_
+from numpy import floating as np_floating
+from numpy import generic as np_generic
+from numpy import integer as np_integer
+from numpy import number as np_number
+from pandas import DataFrame, Series
+from sys import float_info as sflt
+
+
+# Generic types
+T = TypeVar("T")
+
+# Scalars
+Scalar = Union[str, float, int, complex, bool, object, np_generic]
+Number = Union[int, float, complex, np_number, np_bool_]
+Int = Union[int, np_integer]
+Float = Union[float, np_floating]
+IntFloat = Union[Int, Float]
+
+# Basic sequences
+MaybeTuple = Union[T, Tuple[T, ...]]
+MaybeList = Union[T, List[T]]
+TupleList = Union[List[T], Tuple[T, ...]]
+MaybeTupleList = Union[T, List[T], Tuple[T, ...]]
+MaybeIterable = Union[T, Iterable[T]]
+MaybeSequence = Union[T, Sequence[T]]
+ListStr = List[str]
+
+DictLike = Union[None, dict]
+DictLikeSequence = MaybeSequence[DictLike]
+Args = Tuple[Any, ...]
+ArgsLike = Union[None, Args]
+Kwargs = Dict[str, Any]
+KwargsLike = Union[None, Kwargs]
+KwargsLikeSequence = MaybeSequence[KwargsLike]
+FileName = Union[str, Path]
+
+DTypeLike = Any
+PandasDTypeLike = Any
+Shape = Tuple[int, ...]
+RelaxedShape = Union[int, Shape]
+Array = ndarray  # ready to be used for n-dim data
+Array1d = ndarray
+Array2d = ndarray
+Array3d = ndarray
+Record = void
+RecordArray = ndarray
+RecArray = recarray
+MaybeArray = Union[T, Array]
+SeriesFrame = Union[Series, DataFrame]
+MaybeSeries = Union[T, Series]
+MaybeSeriesFrame = Union[T, Series, DataFrame]
+AnyArray = Union[Array, Series, DataFrame]
+AnyArray1d = Union[Array1d, Series]
+AnyArray2d = Union[Array2d, DataFrame]

pandas_ta/candles/cdl_doji.py

@@ -1,15 +1,16 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
 from pandas_ta.overlap import sma
+from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.utils import get_offset, high_low_range, is_percent
 from pandas_ta.utils import real_body, verify_series
 
 
 def cdl_doji(
     open_: Series, high: Series, low: Series, close: Series,
-    length: int = None, factor: float = None, scalar: float = None,
+    length: Int = None, factor: IntFloat = None, scalar: IntFloat = None,
     asint: bool = True,
-    offset: int = None, **kwargs
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Candle Type: Doji
 

pandas_ta/candles/cdl_inside.py

@@ -1,12 +1,13 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.utils import candle_color, get_offset, verify_series
 
 
 def cdl_inside(
     open_: Series, high: Series, low: Series, close: Series,
     asbool: bool = False,
-    offset: int = None, **kwargs
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Candle Type: Inside Bar
 

pandas_ta/candles/cdl_pattern.py

@@ -1,6 +1,6 @@
 # -*- coding: utf-8 -*-
-from typing import Sequence, Union
 from pandas import Series, DataFrame
+from pandas_ta._typing import DictLike, Int, IntFloat, List, Union
 from pandas_ta.maps import Imports
 from pandas_ta.utils import get_offset, verify_series
 from pandas_ta.candles import cdl_doji, cdl_inside
@@ -24,8 +24,8 @@ ALL_PATTERNS = [
 
 def cdl_pattern(
     open_: Series, high: Series, low: Series, close: Series,
-    name: Union[str, Sequence[str]] = "all", scalar: float = None,
-    offset: int = None, **kwargs
+    name: Union[str, List[str]] = "all", scalar: IntFloat = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> DataFrame:
     """TA Lib Candle Patterns
 
@@ -89,6 +89,8 @@ def cdl_pattern(
         if n in pta_patterns:
             pattern_result = pta_patterns[n](
                 open_, high, low, close, offset=offset, scalar=scalar, **kwargs)
+            if not isinstance(pattern_result,Series):
+                continue
             result[pattern_result.name] = pattern_result
         else:
             if not Imports["talib"]:
@@ -96,16 +98,10 @@ def cdl_pattern(
                     f"[X] Please install TA-Lib to use {n}. (pip install TA-Lib)")
                 continue
 
-            pattern_func = tala.Function(f"CDL{n.upper()}")
+            pf = tala.Function(f"CDL{n.upper()}")
             pattern_result = Series(
-                pattern_func(
-                    open_,
-                    high,
-                    low,
-                    close,
-                    **kwargs) /
-                100 *
-                scalar)
+                0.01 * scalar * pf(open_, high, low, close, **kwargs)
+            )
             pattern_result.index = close.index
 
             # Offset
@@ -130,5 +126,4 @@ def cdl_pattern(
     df.category = "candles"
     return df
 
-
 cdl = cdl_pattern  # Alias

pandas_ta/candles/cdl_z.py

@@ -1,13 +1,14 @@
 # -*- coding: utf-8 -*-
 from pandas import DataFrame, Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.statistics import zscore
 from pandas_ta.utils import get_offset, verify_series
 
 
 def cdl_z(
     open_: Series, high: Series, low: Series, close: Series,
-    length: int = None, full: bool = None, ddof=None,
-    offset: int = None, **kwargs
+    length: Int = None, full: bool = None, ddof: Int = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> DataFrame:
     """Candle Type: Z
 
@@ -21,6 +22,8 @@ def cdl_z(
         low (pd.Series): Series of 'low's
         close (pd.Series): Series of 'close's
         length (int): The period. Default: 10
+        full (bool): Apply to whole DataFrame. Default: False
+        ddof (int): Degrees of Freedom. Default: 1
 
     Kwargs:
         naive (bool, optional): If True, prefills potential Doji less than

pandas_ta/candles/ha.py

@@ -1,11 +1,12 @@
 # -*- coding: utf-8 -*-
 from pandas import DataFrame, Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.utils import get_offset, verify_series
 
 
 def ha(
     open_: Series, high: Series, low: Series, close: Series,
-    offset: int = None, **kwargs
+    offset: Int = None, **kwargs: DictLike
 ) -> DataFrame:
     """Heikin Ashi Candles (HA)
 

pandas_ta/custom.py

@@ -8,6 +8,7 @@ from os.path import abspath, join, exists, basename, splitext
 from glob import glob
 
 import pandas_ta
+from pandas_ta._typing import DictLike
 
 
 def bind(name: str, f: types.FunctionType):#, method: types.MethodType = None):
@@ -56,9 +57,10 @@ def create_dir(path: str, create_categories: bool = True, verbose: bool = True):
                     print(f"[i] Created an empty sub-directory '{dirname}'.")
 
 
-def get_module_functions(module: types.ModuleType) -> dict:
+def get_module_functions(module: types.ModuleType) -> DictLike:
     """
-     Helper function to get the functions of an imported module as a dictionary.
+     Helper function to get the functions of an imported module
+     as a dictionary.
 
     Args:
         module: python module
@@ -87,14 +89,14 @@ def import_dir(path: str, verbose: bool = True):
         path (str): Full path to your indicator tree
         verbose (bool): If True verbose output of results
 
-    This method allows you to experiment and develop your own technical analysis
-    indicators in a separate local directory of your choice but use them seamlessly
-    together with the existing pandas_ta functions just like if they were part of
-    pandas_ta.
+    This method allows you to experiment and develop your own technical
+    analysis indicators in a separate local directory of your choice but
+    use them seamlessly together with the existing pandas_ta functions just
+    like if they were part of pandas_ta.
 
-    If you at some late point would like to push them into the pandas_ta library
-    you can do so very easily by following the step by step instruction here
-    https://github.com/twopirllc/pandas-ta/issues/355.
+    If you at some late point would like to push them into the pandas_ta
+    library you can do so very easily by following the step by step
+    instruction here https://github.com/twopirllc/pandas-ta/issues/355.
 
     A brief example of usage:
 
@@ -102,9 +104,9 @@ def import_dir(path: str, verbose: bool = True):
     >>> import pandas as pd
     >>> import pandas_ta as ta
 
-    2. Create an empty directory on your machine where you want to work with your
-    indicators. Invoke pandas_ta.custom.import_dir once to pre-populate it with
-    sub-folders for all available indicator categories, e.g.:
+    2. Create an empty directory on your machine where you want to work with
+    your indicators. Invoke pandas_ta.custom.import_dir once to pre-populate
+    it with sub-folders for all available indicator categories, e.g.:
 
     >>> import os
     >>> from os.path import abspath, join, expanduser
@@ -121,8 +123,8 @@ def import_dir(path: str, verbose: bool = True):
     ending with '_method'. E.g. 'ni_method'
 
     In essence these modules should look exactly like the standard indicators
-    available in categories under the pandas_ta-folder. The only difference will
-    be an addition of a matching class method.
+    available in categories under the pandas_ta-folder. The only difference
+    will be an addition of a matching class method.
 
     For an example of the correct structure, look at the example ni.py in the
     examples folder.

pandas_ta/cycles/ebsw.py

@@ -1,13 +1,14 @@
 # -*- coding: utf-8 -*-
 from numpy import cos, exp, mean, nan, pi, roll, sin, sqrt, zeros
 from pandas import Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.utils import get_offset, verify_series
 
 
 def ebsw(
-    close: Series, length: int = None, bars: int = None,
+    close: Series, length: Int = None, bars: Int = None,
     initial_version: bool = False,
-    offset: int = None, **kwargs
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Even Better SineWave (EBSW)
 

pandas_ta/cycles/reflex.py

@@ -1,6 +1,7 @@
 # -*- coding: utf-8 -*-
-from numpy import cos, exp, nan, ndarray, sqrt, zeros_like
+from numpy import cos, exp, nan, sqrt, zeros_like
 from pandas import Series
+from pandas_ta._typing import Array, DictLike, Int, IntFloat
 from pandas_ta.utils import get_offset, verify_series
 
 
@@ -11,8 +12,9 @@ except ImportError:
 
 
 @njit
-def np_reflex(x: ndarray, n: int, k: int,
-              alpha: float, pi: float, sqrt2: float):
+def np_reflex(
+    x: Array, n: Int, k: Int, alpha: IntFloat, pi: IntFloat, sqrt2: IntFloat
+):
     m, ratio = x.size, 2 * sqrt2 / k
     a = exp(-pi * ratio)
     b = 2 * a * cos(180 * ratio)
@@ -41,10 +43,10 @@ def np_reflex(x: ndarray, n: int, k: int,
 
 
 def reflex(
-    close: Series, length: int = None,
-    smooth: int = None, alpha: float = None,
-    pi: float = None, sqrt2: float = None,
-    offset: int = None, **kwargs
+    close: Series, length: Int = None,
+    smooth: Int = None, alpha: IntFloat = None,
+    pi: IntFloat = None, sqrt2: IntFloat = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Reflex (reflex)
 

pandas_ta/ma.py

@@ -1,5 +1,6 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.overlap.dema import dema
 from pandas_ta.overlap.ema import ema
 from pandas_ta.overlap.fwma import fwma
@@ -19,7 +20,7 @@ from pandas_ta.overlap.vidya import vidya
 from pandas_ta.overlap.wma import wma
 
 
-def ma(name: str = None, source: Series = None, **kwargs) -> Series:
+def ma(name: str = None, source: Series = None, **kwargs: DictLike) -> Series:
     """Simple MA Utility for easier MA selection
 
     Available MAs:

pandas_ta/maps.py

@@ -3,6 +3,8 @@ from importlib.util import find_spec
 from pathlib import Path
 from pkg_resources import get_distribution, DistributionNotFound
 
+from pandas_ta._typing import Dict, IntFloat, ListStr
+
 
 _dist = get_distribution("pandas_ta")
 try:
@@ -16,7 +18,7 @@ except DistributionNotFound:
 
 version = __version__ = _dist.version
 
-Imports = {
+Imports: Dict[str, bool] = {
     "alphaVantage-api": find_spec("alphaVantageAPI") is not None,
     "dotenv": find_spec("dotenv") is not None,
     "matplotlib": find_spec("matplotlib") is not None,
@@ -36,7 +38,7 @@ Imports = {
 
 # Not ideal and not dynamic but it works.
 # Will find a dynamic solution later.
-Category = {
+Category: Dict[str, ListStr] = {
     # Candles
     "candles": [
         "cdl_pattern", "cdl_z", "ha"
@@ -88,7 +90,7 @@ Category = {
     ],
 }
 
-CANDLE_AGG = {
+CANDLE_AGG: Dict[str, str] = {
     "open": "first",
     "high": "max",
     "low": "min",
@@ -97,7 +99,7 @@ CANDLE_AGG = {
 }
 
 # https://www.worldtimezone.com/markets24.php
-EXCHANGE_TZ = {
+EXCHANGE_TZ: Dict[str, IntFloat] = {
     "NZSX": 12, "ASX": 11,
     "TSE": 9, "HKE": 8, "SSE": 8, "SGX": 8,
     "NSE": 5.5, "DIFX": 4, "RTS": 3,
@@ -106,7 +108,7 @@ EXCHANGE_TZ = {
     "GENR": 0 # Generated Data
 }
 
-RATE = {
+RATE: Dict[str, IntFloat] = {
     "DAYS_PER_MONTH": 21,
     "MINUTES_PER_HOUR": 60,
     "MONTHS_PER_YEAR": 12,

pandas_ta/momentum/ao.py

@@ -1,12 +1,13 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.overlap import sma
 from pandas_ta.utils import get_offset, verify_series
 
 
 def ao(
-    high: Series, low: Series, fast: int = None, slow: int = None,
-    offset: int = None, **kwargs
+    high: Series, low: Series, fast: Int = None, slow: Int = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Awesome Oscillator (AO)
 

pandas_ta/momentum/apo.py

@@ -1,14 +1,15 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.ma import ma
 from pandas_ta.maps import Imports
 from pandas_ta.utils import get_offset, tal_ma, verify_series
 
 
 def apo(
-    close: Series, fast: int = None, slow: int = None,
+    close: Series, fast: Int = None, slow: Int = None,
     mamode: str = None, talib: bool = None,
-    offset: int = None, **kwargs
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Absolute Price Oscillator (APO)
 

pandas_ta/momentum/bias.py

@@ -1,12 +1,13 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.ma import ma
 from pandas_ta.utils import get_offset, verify_series
 
 
 def bias(
-    close: Series, length: int = None, mamode: str = None,
-    offset: int = None, **kwargs
+    close: Series, length: Int = None, mamode: str = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Bias (BIAS)
 

pandas_ta/momentum/bop.py

@@ -1,13 +1,14 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
+from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.maps import Imports
 from pandas_ta.utils import get_offset, non_zero_range, verify_series
 
 
 def bop(
     open_: Series, high: Series, low: Series, close: Series,
-    scalar: float = None, talib: bool = None,
-    offset: int = None, **kwargs
+    scalar: IntFloat = None, talib: bool = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Balance of Power (BOP)
 

pandas_ta/momentum/brar.py

@@ -1,12 +1,13 @@
 # -*- coding: utf-8 -*-
 from pandas import DataFrame, Series
+from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.utils import get_drift, get_offset, non_zero_range, verify_series
 
 
 def brar(
     open_: Series, high: Series, low: Series, close: Series,
-    length: int = None, scalar: float = None, drift: int = None,
-    offset: int = None, **kwargs
+    length: Int = None, scalar: IntFloat = None, drift: Int = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> DataFrame:
     """BRAR (BRAR)
 

pandas_ta/momentum/cci.py

@@ -1,5 +1,6 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
+from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.maps import Imports
 from pandas_ta.overlap import hlc3, sma
 from pandas_ta.statistics import mad
@@ -7,9 +8,9 @@ from pandas_ta.utils import get_offset, verify_series
 
 
 def cci(
-    high: Series, low: Series, close: Series, length: int = None,
-    c: float = None, talib: bool = None,
-    offset: int = None, **kwargs
+    high: Series, low: Series, close: Series, length: Int = None,
+    c: IntFloat = None, talib: bool = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Commodity Channel Index (CCI)
 

pandas_ta/momentum/cfo.py

@@ -1,13 +1,14 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
+from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.overlap import linreg
 from pandas_ta.utils import get_drift, get_offset, verify_series
 
 
 def cfo(
-    close: Series, length: int = None,
-    scalar: float = None, drift: int = None,
-    offset: int = None, **kwargs
+    close: Series, length: Int = None,
+    scalar: IntFloat = None, drift: Int = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Chande Forcast Oscillator (CFO)
 

pandas_ta/momentum/cg.py

@@ -1,11 +1,12 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.utils import get_offset, verify_series, weights
 
 
 def cg(
-    close: Series, length: int = None,
-    offset: int = None, **kwargs
+    close: Series, length: Int = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Center of Gravity (CG)
 

pandas_ta/momentum/cmo.py

@@ -1,14 +1,15 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
+from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.maps import Imports
 from pandas_ta.overlap import rma
 from pandas_ta.utils import get_drift, get_offset, verify_series
 
 
 def cmo(
-    close: Series, length: int = None, scalar: float = None,
-    talib: bool = None, drift: int = None,
-    offset: int = None, **kwargs
+    close: Series, length: Int = None, scalar: IntFloat = None,
+    talib: bool = None, drift: Int = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Chande Momentum Oscillator (CMO)
 

pandas_ta/momentum/coppock.py

@@ -1,13 +1,14 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.overlap import wma
 from pandas_ta.utils import get_offset, verify_series
 from .roc import roc
 
 
 def coppock(
-    close: Series, length: int = None, fast: int = None, slow: int = None,
-    offset: int = None, **kwargs
+    close: Series, length: Int = None, fast: Int = None, slow: Int = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Coppock Curve (COPC)
 

pandas_ta/momentum/cti.py

@@ -1,12 +1,13 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.overlap import linreg
 from pandas_ta.utils import get_offset, verify_series
 
 
 def cti(
-    close: Series, length: int = None,
-    offset: int = None, **kwargs
+    close: Series, length: Int = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Correlation Trend Indicator (CTI)
 

pandas_ta/momentum/dm.py

@@ -1,14 +1,15 @@
 # -*- coding: utf-8 -*-
 from pandas import DataFrame, Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.ma import ma
 from pandas_ta.maps import Imports
 from pandas_ta.utils import get_offset, verify_series, get_drift, zero
 
 
 def dm(
-    high: Series, low: Series, length: int = None,
-    mamode: str = None, talib: bool = None, drift: int = None,
-    offset: int = None, **kwargs
+    high: Series, low: Series, length: Int = None,
+    mamode: str = None, talib: bool = None, drift: Int = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> DataFrame:
     """Directional Movement (DM)
 

pandas_ta/momentum/er.py

@@ -1,11 +1,12 @@
 # -*- coding: utf-8 -*-
 from pandas import DataFrame, concat, Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.utils import get_drift, get_offset, verify_series, signals
 
 
 def er(
-    close: Series, length: int = None, drift: int = None,
-    offset: int = None, **kwargs
+    close: Series, length: Int = None, drift: Int = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Efficiency Ratio (ER)
 

pandas_ta/momentum/eri.py

@@ -1,12 +1,13 @@
 # -*- coding: utf-8 -*-
 from pandas import DataFrame, Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.overlap import ema
 from pandas_ta.utils import get_offset, verify_series
 
 
 def eri(
-    high: Series, low: Series, close: Series, length: int = None,
-    offset: int = None, **kwargs
+    high: Series, low: Series, close: Series, length: Int = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> DataFrame:
     """Elder Ray Index (ERI)
 

pandas_ta/momentum/fisher.py

@@ -1,13 +1,14 @@
 # -*- coding: utf-8 -*-
 from numpy import log, nan
 from pandas import DataFrame, Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.overlap import hl2
 from pandas_ta.utils import get_offset, high_low_range, verify_series
 
 
 def fisher(
-    high: Series, low: Series, length: int = None, signal: int = None,
-    offset: int = None, **kwargs
+    high: Series, low: Series, length: Int = None, signal: Int = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Fisher Transform (FISHT)
 

pandas_ta/momentum/inertia.py

@@ -1,5 +1,6 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
+from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.overlap import linreg
 from pandas_ta.utils import get_drift, get_offset, verify_series
 from pandas_ta.volatility import rvi
@@ -7,10 +8,10 @@ from pandas_ta.volatility import rvi
 
 def inertia(
     close: Series, high: Series = None, low: Series = None,
-    length: int = None, rvi_length: int = None, scalar: float = None,
+    length: Int = None, rvi_length: Int = None, scalar: IntFloat = None,
     refined: bool = None, thirds: bool = None,
-    drift: int = None, mamode: str = None,
-    offset: int = None, **kwargs
+    drift: Int = None, mamode: str = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Inertia (INERTIA)
 

pandas_ta/momentum/kdj.py

@@ -1,12 +1,13 @@
 # -*- coding: utf-8 -*-
 from pandas import DataFrame, Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.utils import get_offset, non_zero_range, rma_pandas, verify_series
 
 
 def kdj(
     high: Series, low: Series, close: Series,
-    length: int = None, signal: int = None,
-    offset: int = None, **kwargs
+    length: Int = None, signal: Int = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """KDJ (KDJ)
 

pandas_ta/momentum/kst.py

@@ -1,15 +1,16 @@
 # -*- coding: utf-8 -*-
 from pandas import DataFrame, Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.utils import get_drift, get_offset, verify_series
 from .roc import roc
 
 
 def kst(
-    close: Series, signal: int = None,
-    roc1: int = None, roc2: int = None, roc3: int = None, roc4: int = None,
-    sma1: int = None, sma2: int = None, sma3: int = None, sma4: int = None,
-    drift: int = None,
-    offset: int = None, **kwargs
+    close: Series, signal: Int = None,
+    roc1: Int = None, roc2: Int = None, roc3: Int = None, roc4: Int = None,
+    sma1: Int = None, sma2: Int = None, sma3: Int = None, sma4: Int = None,
+    drift: Int = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> DataFrame:
     """'Know Sure Thing' (KST)
 

pandas_ta/momentum/macd.py

@@ -1,13 +1,14 @@
 # -*- coding: utf-8 -*-
 from pandas import concat, DataFrame, Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.maps import Imports
 from pandas_ta.overlap import ema
 from pandas_ta.utils import get_offset, verify_series, signals
 
 
 def macd(
-    close: Series, fast: int = None, slow: int = None, signal: int = None,
-    talib: bool = None, offset: int = None, **kwargs
+    close: Series, fast: Int = None, slow: Int = None, signal: Int = None,
+    talib: bool = None, offset: Int = None, **kwargs: DictLike
 ) -> DataFrame:
     """Moving Average Convergence Divergence (MACD)
 

pandas_ta/momentum/mom.py

@@ -1,12 +1,13 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.maps import Imports
 from pandas_ta.utils import get_offset, verify_series
 
 
 def mom(
-    close: Series, length: int = None, talib: bool = None,
-    offset: int = None, **kwargs
+    close: Series, length: Int = None, talib: bool = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Momentum (MOM)
 

pandas_ta/momentum/pgo.py

@@ -1,13 +1,14 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.overlap import ema, sma
 from pandas_ta.utils import get_offset, verify_series
 from pandas_ta.volatility import atr
 
 
 def pgo(
-    high: Series, low: Series, close: Series, length: int = None,
-    offset: int = None, **kwargs
+    high: Series, low: Series, close: Series, length: Int = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Pretty Good Oscillator (PGO)
 

pandas_ta/momentum/ppo.py

@@ -1,14 +1,15 @@
 # -*- coding: utf-8 -*-
 from pandas import DataFrame, Series
+from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.ma import ma
 from pandas_ta.maps import Imports
 from pandas_ta.utils import get_offset, tal_ma, verify_series
 
 
 def ppo(
-    close: Series, fast: int = None, slow: int = None, signal: int = None,
-    scalar: float = None, mamode: str = None, talib: bool = None,
-    offset: int = None, **kwargs
+    close: Series, fast: Int = None, slow: Int = None, signal: Int = None,
+    scalar: IntFloat = None, mamode: str = None, talib: bool = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> DataFrame:
     """Percentage Price Oscillator (PPO)
 

pandas_ta/momentum/psl.py

@@ -1,13 +1,14 @@
 # -*- coding: utf-8 -*-
 from numpy import sign
 from pandas import Series
+from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.utils import get_drift, get_offset, verify_series
 
 
 def psl(
     close: Series, open_: Series = None,
-    length: int = None, scalar: float = None, drift: int = None,
-    offset: int = None, **kwargs
+    length: Int = None, scalar: IntFloat = None, drift: Int = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Psychological Line (PSL)
 

pandas_ta/momentum/pvo.py

@@ -1,13 +1,14 @@
 # -*- coding: utf-8 -*-
 from pandas import DataFrame, Series
+from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.overlap import ema
 from pandas_ta.utils import get_offset, verify_series
 
 
 def pvo(
-    volume: Series, fast: int = None, slow: int = None, signal: int = None,
-    scalar: float = None,
-    offset: int = None, **kwargs
+    volume: Series, fast: Int = None, slow: Int = None,
+    signal: Int = None, scalar: IntFloat = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> DataFrame:
     """Percentage Volume Oscillator (PVO)
 

pandas_ta/momentum/qqe.py

@@ -1,16 +1,17 @@
 # -*- coding: utf-8 -*-
 from numpy import isnan, maximum, minimum, nan
 from pandas import DataFrame, Series
+from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.ma import ma
 from pandas_ta.utils import get_drift, get_offset, verify_series
 from .rsi import rsi
 
 
 def qqe(
-    close: Series, length: int = None,
-    smooth: int = None, factor: float = None,
-    mamode: str = None, drift: int = None,
-    offset: int = None, **kwargs
+    close: Series, length: Int = None,
+    smooth: Int = None, factor: IntFloat = None,
+    mamode: str = None, drift: Int = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> DataFrame:
     """Quantitative Qualitative Estimation (QQE)
 

pandas_ta/momentum/roc.py

@@ -1,14 +1,15 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
+from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.maps import Imports
 from pandas_ta.utils import get_offset, verify_series
 from .mom import mom
 
 
 def roc(
-    close: Series, length: int = None,
-    scalar: float = None, talib: bool = None,
-    offset: int = None, **kwargs
+    close: Series, length: Int = None,
+    scalar: IntFloat = None, talib: bool = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Rate of Change (ROC)
 

pandas_ta/momentum/rsi.py

@@ -1,14 +1,15 @@
 # -*- coding: utf-8 -*-
 from pandas import DataFrame, concat, Series
+from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.maps import Imports
 from pandas_ta.overlap import rma
 from pandas_ta.utils import get_drift, get_offset, verify_series, signals
 
 
 def rsi(
-    close: Series, length: int = None, scalar: float = None,
-    talib: bool = None, drift: int = None,
-    offset: int = None, **kwargs
+    close: Series, length: Int = None, scalar: IntFloat = None,
+    talib: bool = None, drift: Int = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Relative Strength Index (RSI)
 

pandas_ta/momentum/rsx.py

@@ -1,12 +1,13 @@
 # -*- coding: utf-8 -*-
 from numpy import nan
+from pandas_ta._typing import DictLike, Int
 from pandas import concat, DataFrame, Series
 from pandas_ta.utils import get_drift, get_offset, verify_series, signals
 
 
 def rsx(
-    close: Series, length: int = None, drift: int = None,
-    offset: int = None, **kwargs
+    close: Series, length: Int = None, drift: Int = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Relative Strength Xtra (rsx)
 

pandas_ta/momentum/rvgi.py

@@ -1,13 +1,14 @@
 # -*- coding: utf-8 -*-
 from pandas import DataFrame, Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.overlap import swma
 from pandas_ta.utils import get_offset, non_zero_range, verify_series
 
 
 def rvgi(
     open_: Series, high: Series, low: Series, close: Series,
-    length: int = None, swma_length: int = None,
-    offset: int = None, **kwargs
+    length: Int = None, swma_length: Int = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Relative Vigor Index (RVGI)
 

pandas_ta/momentum/slope.py

@@ -1,13 +1,14 @@
 # -*- coding: utf-8 -*-
 from numpy import arctan, pi
 from pandas import Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.utils import get_offset, verify_series
 
 
 def slope(
-    close: Series, length: int = None,
-    as_angle=None, to_degrees=None, vertical=None,
-    offset: int = None, **kwargs
+    close: Series, length: Int = None,
+    as_angle: bool = None, to_degrees: bool = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Slope
 

pandas_ta/momentum/smi.py

@@ -1,13 +1,14 @@
 # -*- coding: utf-8 -*-
 from pandas import DataFrame, Series
+from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.utils import get_offset, verify_series
 from .tsi import tsi
 
 
 def smi(
-    close: Series, fast: int = None, slow: int = None, signal: int = None,
-    scalar: float = None,
-    offset: int = None, **kwargs
+    close: Series, fast: Int = None, slow: Int = None,
+    signal: Int = None, scalar: IntFloat = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> DataFrame:
     """SMI Ergodic Indicator (SMI)
 

pandas_ta/momentum/squeeze.py

@@ -1,6 +1,7 @@
 # -*- coding: utf-8 -*-
 from numpy import nan
 from pandas import DataFrame, Series
+from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.overlap import ema, linreg, sma
 from pandas_ta.trend import decreasing, increasing
 from pandas_ta.utils import get_offset, simplify_columns, unsigned_differences, verify_series
@@ -10,11 +11,11 @@ from .mom import mom
 
 def squeeze(
     high: Series, low: Series, close: Series,
-    bb_length: int = None, bb_std: float = None,
-    kc_length: int = None, kc_scalar: float = None,
-    mom_length: int = None, mom_smooth: int = None,
-    use_tr=None, mamode: str = None,
-    offset: int = None, **kwargs
+    bb_length: Int = None, bb_std: IntFloat = None,
+    kc_length: Int = None, kc_scalar: IntFloat = None,
+    mom_length: Int = None, mom_smooth: Int = None,
+    use_tr: bool = None, mamode: str = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> DataFrame:
     """Squeeze (SQZ)
 

pandas_ta/momentum/squeeze_pro.py

@@ -1,6 +1,7 @@
 # -*- coding: utf-8 -*-
 from numpy import nan
 from pandas import DataFrame, Series
+from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.momentum import mom
 from pandas_ta.overlap import ema, sma
 from pandas_ta.trend import decreasing, increasing
@@ -10,12 +11,12 @@ from pandas_ta.utils import get_offset, simplify_columns, unsigned_differences,
 
 def squeeze_pro(
     high: Series, low: Series, close: Series,
-    bb_length: int = None, bb_std: float = None,
-    kc_length: int = None, kc_scalar_wide: float = None,
-    kc_scalar_normal: float = None, kc_scalar_narrow: float = None,
-    mom_length: int = None, mom_smooth: int = None,
-    use_tr=None, mamode: str = None,
-    offset: int = None, **kwargs
+    bb_length: Int = None, bb_std: IntFloat = None,
+    kc_length: Int = None, kc_scalar_wide: IntFloat = None,
+    kc_scalar_normal: IntFloat = None, kc_scalar_narrow: IntFloat = None,
+    mom_length: Int = None, mom_smooth: Int = None,
+    use_tr: bool = None, mamode: str = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> DataFrame:
     """Squeeze PRO(SQZPRO)
 
@@ -94,7 +95,8 @@ def squeeze_pro(
     close = verify_series(close, _length)
     offset = get_offset(offset)
 
-    valid_kc_scaler = kc_scalar_wide > kc_scalar_normal and kc_scalar_normal > kc_scalar_narrow
+    valid_kc_scaler = kc_scalar_wide > kc_scalar_normal \
+        and kc_scalar_normal > kc_scalar_narrow
 
     if not valid_kc_scaler:
         return

pandas_ta/momentum/stc.py

@@ -1,13 +1,14 @@
 # -*- coding: utf-8 -*-
 from pandas import DataFrame, Series
+from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.overlap import ema
 from pandas_ta.utils import get_offset, non_zero_range, verify_series
 
 
 def stc(
-    close: Series, tclength: int = None,
-    fast: int = None, slow: int = None, factor: float = None,
-    offset: int = None, **kwargs
+    close: Series, tclength: Int = None,
+    fast: Int = None, slow: Int = None, factor: IntFloat = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> DataFrame:
     """Schaff Trend Cycle (STC)
 
@@ -29,7 +30,8 @@ def stc(
         extMa2 = df.ta.ema(close=df["close"], length=ma2_interval, append=True)
         stc = ta.stc(close=df["close"], tclen=stc_tclen, ma1=extMa1, ma2=extMa2, factor=stc_factor)
 
-    The same goes for osc=, which allows the input of an externally calculated oscillator, overriding ma1 & ma2.
+    The same goes for osc=, which allows the input of an externally
+    calculated oscillator, overriding ma1 & ma2.
 
     Sources:
         Implemented by rengel8 based on work found here:

pandas_ta/momentum/stoch.py

@@ -1,5 +1,6 @@
 # -*- coding: utf-8 -*-
 from pandas import DataFrame, Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.ma import ma
 from pandas_ta.maps import Imports
 from pandas_ta.utils import get_offset, non_zero_range, tal_ma, verify_series
@@ -7,9 +8,9 @@ from pandas_ta.utils import get_offset, non_zero_range, tal_ma, verify_series
 
 def stoch(
     high: Series, low: Series, close: Series,
-    k: int = None, d: int = None, smooth_k: int = None,
+    k: Int = None, d: Int = None, smooth_k: Int = None,
     mamode: str = None, talib: bool = None,
-    offset: int = None, **kwargs
+    offset: Int = None, **kwargs: DictLike
 ) -> DataFrame:
     """Stochastic (STOCH)
 

pandas_ta/momentum/stochf.py

@@ -1,14 +1,16 @@
 # -*- coding: utf-8 -*-
 from pandas import DataFrame, Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.ma import ma
 from pandas_ta.maps import Imports
 from pandas_ta.utils import get_offset, non_zero_range, tal_ma, verify_series
 
 
 def stochf(
-    high: Series, low: Series, close: Series, k: int = None, d: int = None,
+    high: Series, low: Series, close: Series,
+    k: Int = None, d: Int = None,
     mamode: str = None, talib: bool = None,
-    offset: int = None, **kwargs
+    offset: Int = None, **kwargs: DictLike
 ) -> DataFrame:
     """Fast Stochastic (STOCHF)
 

pandas_ta/momentum/stochrsi.py

@@ -1,14 +1,15 @@
 # -*- coding: utf-8 -*-
 from pandas import DataFrame, Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.ma import ma
 from pandas_ta.momentum import rsi
 from pandas_ta.utils import get_offset, non_zero_range, verify_series
 
 
 def stochrsi(
-    close: Series, length: int = None, rsi_length: int = None,
-    k: int = None, d: int = None, mamode: str = None,
-    offset: int = None, **kwargs
+    close: Series, length: Int = None, rsi_length: Int = None,
+    k: Int = None, d: Int = None, mamode: str = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> DataFrame:
     """Stochastic (STOCHRSI)
 

pandas_ta/momentum/td_seq.py

@@ -1,12 +1,13 @@
 # -*- coding: utf-8 -*-
 from numpy import where
 from pandas import DataFrame, Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.utils import get_offset, verify_series
 
 
 def td_seq(
     close: Series, asint: bool = None, show_all: bool = None,
-    offset: int = None, **kwargs
+    offset: Int = None, **kwargs: DictLike
 ) -> DataFrame:
     """TD Sequential (TD_SEQ)
 

pandas_ta/momentum/trix.py

@@ -1,14 +1,14 @@
 # -*- coding: utf-8 -*-
-# from numpy import isnan
 from pandas import DataFrame, Series
+from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.overlap.ema import ema
 from pandas_ta.utils import get_drift, get_offset, verify_series
 
 
 def trix(
-    close: Series, length: int = None, signal: int = None,
-    scalar: float = None, drift: int = None,
-    offset: int = None, **kwargs
+    close: Series, length: Int = None, signal: Int = None,
+    scalar: IntFloat = None, drift: Int = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Trix (TRIX)
 

pandas_ta/momentum/tsi.py

@@ -1,14 +1,16 @@
 # -*- coding: utf-8 -*-
 from pandas import DataFrame, Series
+from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.ma import ma
 from pandas_ta.overlap import ema
 from pandas_ta.utils import get_drift, get_offset, verify_series
 
 
 def tsi(
-    close: Series, fast: int = None, slow: int = None, signal: int = None,
-    scalar: float = None, mamode: str = None, drift: int = None,
-    offset: int = None, **kwargs
+    close: Series, fast: Int = None, slow: Int = None,
+    signal: Int = None, scalar: IntFloat = None,
+    mamode: str = None, drift: Int = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> DataFrame:
     """True Strength Index (TSI)
 

pandas_ta/momentum/uo.py

@@ -1,15 +1,16 @@
 # -*- coding: utf-8 -*-
 from pandas import DataFrame, Series
+from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.maps import Imports
 from pandas_ta.utils import get_drift, get_offset, verify_series
 
 
 def uo(
     high: Series, low: Series, close: Series,
-    fast: int = None, medium: int = None, slow: int = None,
-    fast_w: float = None, medium_w: float = None, slow_w: float = None,
-    talib: bool = None, drift: int = None,
-    offset: int = None, **kwargs
+    fast: Int = None, medium: Int = None, slow: Int = None,
+    fast_w: IntFloat = None, medium_w: IntFloat = None, slow_w: IntFloat = None,
+    talib: bool = None, drift: Int = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Ultimate Oscillator (UO)
 

pandas_ta/momentum/willr.py

@@ -1,13 +1,14 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.maps import Imports
 from pandas_ta.utils import get_offset, verify_series
 
 
 def willr(
     high: Series, low: Series, close: Series,
-    length: int = None, talib: bool = None,
-    offset: int = None, **kwargs
+    length: Int = None, talib: bool = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """William's Percent R (WILLR)
 

pandas_ta/overlap/alligator.py

@@ -1,13 +1,14 @@
 # -*- coding: utf-8 -*-
 from pandas import DataFrame, Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.utils import get_offset, verify_series
 from .smma import smma
 
 
 def alligator(
-    close: Series, jaw: int = None, teeth: int = None, lips: int = None,
+    close: Series, jaw: Int = None, teeth: Int = None, lips: Int = None,
     talib: bool = None,
-    offset: int = None, **kwargs
+    offset: Int = None, **kwargs: DictLike
 ) -> DataFrame:
     """Bill Williams Alligator (ALLIGATOR)
 

pandas_ta/overlap/alma.py

@@ -1,14 +1,15 @@
 # -*- coding: utf-8 -*-
 from numpy import append, arange, array, exp, floor, nan, tensordot
 from numpy.version import version as npVersion
+from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas import Series
 from pandas_ta.utils import get_offset, strided_window, verify_series
 
 
 def alma(
-    close: Series, length: int = None,
-    sigma: float = None, dist_offset: float = None,
-    offset: int = None, **kwargs
+    close: Series, length: Int = None,
+    sigma: IntFloat = None, dist_offset: IntFloat = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Arnaud Legoux Moving Average (ALMA)
 

pandas_ta/overlap/dema.py

@@ -1,13 +1,14 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.maps import Imports
 from pandas_ta.utils import get_offset, verify_series
 from .ema import ema
 
 
 def dema(
-    close: Series, length: int = None, talib: bool = None,
-    offset: int = None, **kwargs
+    close: Series, length: Int = None, talib: bool = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Double Exponential Moving Average (DEMA)
 

pandas_ta/overlap/ema.py

@@ -1,6 +1,7 @@
 # -*- coding: utf-8 -*-
 from numpy import nan
 from pandas import Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.maps import Imports
 from pandas_ta.utils import get_offset, verify_series
 
@@ -23,9 +24,9 @@ except ImportError:
 
 
 def ema(
-    close: Series, length: int = None,
+    close: Series, length: Int = None,
     talib: bool = None, presma: bool = None,
-    offset: int = None, **kwargs
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Exponential Moving Average (EMA)
 

pandas_ta/overlap/fwma.py

@@ -1,11 +1,12 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.utils import fibonacci, get_offset, verify_series, weights
 
 
 def fwma(
-    close: Series, length: int = None, asc: bool = None,
-    offset: int = None, **kwargs
+    close: Series, length: Int = None, asc: bool = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Fibonacci's Weighted Moving Average (FWMA)
 

pandas_ta/overlap/hilo.py

@@ -1,14 +1,15 @@
 # -*- coding: utf-8 -*-
 from numpy import nan
 from pandas import DataFrame, Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.ma import ma
 from pandas_ta.utils import get_offset, verify_series
 
 
 def hilo(
     high: Series, low: Series, close: Series,
-    high_length: int = None, low_length: int = None, mamode: str = None,
-    offset: int = None, **kwargs
+    high_length: Int = None, low_length: Int = None, mamode: str = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> DataFrame:
     """Gann HiLo Activator(HiLo)
 

pandas_ta/overlap/hl2.py

@@ -1,11 +1,12 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.utils import get_offset, verify_series
 
 
 def hl2(
     high: Series, low: Series,
-    offset: int = None, **kwargs
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """HL2
 
@@ -16,6 +17,12 @@ def hl2(
         low (pd.Series): Series of 'low's
         offset (int): How many periods to offset the result. Default: 0
 
+    Kwargs:
+        fillna (value, optional): pd.DataFrame.fillna(value). Only works if
+            result is offset.
+        fill_method (value, optional): Type of fill method. Only works if
+            result is offset.
+
     Returns:
         pd.Series: New feature generated.
     """
@@ -32,6 +39,12 @@ def hl2(
     if offset != 0:
         hl2 = hl2.shift(offset)
 
+        # Fill
+        if "fillna" in kwargs:
+            hl2.fillna(kwargs["fillna"], inplace=True)
+        if "fill_method" in kwargs:
+            hl2.fillna(method=kwargs["fill_method"], inplace=True)
+
     # Name and Category
     hl2.name = "HL2"
     hl2.category = "overlap"

pandas_ta/overlap/hlc3.py

@@ -1,12 +1,13 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.maps import Imports
 from pandas_ta.utils import get_offset, verify_series
 
 
 def hlc3(
     high: Series, low: Series, close: Series, talib: bool = None,
-    offset: int = None, **kwargs
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """HLC3
 
@@ -18,6 +19,12 @@ def hlc3(
         close (pd.Series): Series of 'close's
         offset (int): How many periods to offset the result. Default: 0
 
+    Kwargs:
+        fillna (value, optional): pd.DataFrame.fillna(value). Only works if
+            result is offset.
+        fill_method (value, optional): Type of fill method. Only works if
+            result is offset.
+
     Returns:
         pd.Series: New feature generated.
     """
@@ -40,6 +47,12 @@ def hlc3(
     if offset != 0:
         hlc3 = hlc3.shift(offset)
 
+        # Fill
+        if "fillna" in kwargs:
+            hlc3.fillna(kwargs["fillna"], inplace=True)
+        if "fill_method" in kwargs:
+            hlc3.fillna(method=kwargs["fill_method"], inplace=True)
+
     # Name and Category
     hlc3.name = "HLC3"
     hlc3.category = "overlap"

pandas_ta/overlap/hma.py

@@ -1,13 +1,14 @@
 # -*- coding: utf-8 -*-
 from numpy import sqrt
 from pandas import Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.utils import get_offset, verify_series
 from .wma import wma
 
 
 def hma(
-    close: Series, length: int = None,
-    offset: int = None, **kwargs
+    close: Series, length: Int = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Hull Moving Average (HMA)
 

pandas_ta/overlap/hwma.py

@@ -1,11 +1,13 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
+from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.utils import get_offset, verify_series
 
 
 def hwma(
-    close: Series, na: float = None, nb: float = None, nc: float = None,
-    offset: int = None, **kwargs
+    close: Series,
+    na: IntFloat = None, nb: IntFloat = None, nc: IntFloat = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """HWMA (Holt-Winter Moving Average)
 

pandas_ta/overlap/ichimoku.py

@@ -1,14 +1,15 @@
 # -*- coding: utf-8 -*-
 from pandas import date_range, DataFrame, RangeIndex, Timedelta, Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.utils import get_offset, verify_series
 from .midprice import midprice
 
 
 def ichimoku(
     high: Series, low: Series, close: Series,
-    tenkan: int = None, kijun: int = None, senkou: int = None,
+    tenkan: Int = None, kijun: Int = None, senkou: Int = None,
     include_chikou: bool = True,
-    offset: int = None, **kwargs
+    offset: Int = None, **kwargs: DictLike
 ) -> DataFrame:
     """Ichimoku Kinkō Hyō (ichimoku)
 

pandas_ta/overlap/jma.py

@@ -3,12 +3,13 @@
 from numpy import average, log, nan, sqrt, zeros_like
 from numpy import power as np_power
 from pandas import Series
+from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.utils import get_offset, verify_series
 
 
 def jma(
-    close: Series, length: int = None, phase: float = None,
-    offset: int = None, **kwargs
+    close: Series, length: IntFloat = None, phase: IntFloat = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Jurik Moving Average Average (JMA)
 

pandas_ta/overlap/kama.py

@@ -1,14 +1,15 @@
 # -*- coding: utf-8 -*-
 from numpy import nan
 from pandas import Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.ma import ma
 from pandas_ta.utils import get_drift, get_offset, non_zero_range, verify_series
 
 
 def kama(
-    close: Series, length: int = None, fast: int = None, slow: int = None,
-    mamode: str = None, drift: int = None,
-    offset: int = None, **kwargs
+    close: Series, length: Int = None, fast: Int = None, slow: Int = None,
+    mamode: str = None, drift: Int = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Kaufman's Adaptive Moving Average (KAMA)
 

pandas_ta/overlap/linreg.py

@@ -2,13 +2,14 @@
 from numpy import arctan, nan, pi, zeros_like
 from numpy.version import version
 from pandas import Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.maps import Imports
 from pandas_ta.utils import get_offset, strided_window, verify_series
 
 
 def linreg(
-    close: Series, length: int = None, talib: int = None,
-    offset: int = None, **kwargs
+    close: Series, length: Int = None, talib: bool = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Linear Regression Moving Average (linreg)
 

pandas_ta/overlap/mcgd.py

@@ -1,11 +1,12 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
+from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.utils import get_offset, verify_series
 
 
 def mcgd(
-    close: Series, length: int = None, c: float = None,
-    offset: int = None, **kwargs
+    close: Series, length: Int = None, c: IntFloat = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """McGinley Dynamic Indicator
 

pandas_ta/overlap/midpoint.py

@@ -1,12 +1,13 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.maps import Imports
 from pandas_ta.utils import get_offset, verify_series
 
 
 def midpoint(
-    close: Series, length: int = None, talib: bool = None,
-    offset: int = None, **kwargs
+    close: Series, length: Int = None, talib: bool = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Midpoint
 

pandas_ta/overlap/midprice.py

@@ -1,12 +1,13 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.maps import Imports
 from pandas_ta.utils import get_offset, verify_series
 
 
 def midprice(
-    high: Series, low: Series, length: int = None, talib: bool = None,
-    offset: int = None, **kwargs
+    high: Series, low: Series, length: Int = None, talib: bool = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Midprice
 

pandas_ta/overlap/ohlc4.py

@@ -1,11 +1,12 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.utils import get_offset, verify_series
 
 
 def ohlc4(
     open_: Series, high: Series, low: Series, close: Series,
-    offset: int = None, **kwargs
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """OHLC4
 
@@ -18,6 +19,12 @@ def ohlc4(
         close (pd.Series): Series of 'close's
         offset (int): How many periods to offset the result. Default: 0
 
+    Kwargs:
+        fillna (value, optional): pd.DataFrame.fillna(value). Only works if
+            result is offset.
+        fill_method (value, optional): Type of fill method. Only works if
+            result is offset.
+
     Returns:
         pd.Series: New feature generated.
     """
@@ -36,6 +43,12 @@ def ohlc4(
     if offset != 0:
         ohlc4 = ohlc4.shift(offset)
 
+        # Fill
+        if "fillna" in kwargs:
+            ohlc4.fillna(kwargs["fillna"], inplace=True)
+        if "fill_method" in kwargs:
+            ohlc4.fillna(method=kwargs["fill_method"], inplace=True)
+
     # Name and Category
     ohlc4.name = "OHLC4"
     ohlc4.category = "overlap"

pandas_ta/overlap/pwma.py

@@ -1,11 +1,12 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.utils import get_offset, pascals_triangle, verify_series, weights
 
 
 def pwma(
-    close: Series, length: int = None, asc: bool = None,
-    offset: bool = None, **kwargs
+    close: Series, length: Int = None, asc: bool = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Pascal's Weighted Moving Average (PWMA)
 

pandas_ta/overlap/rma.py

@@ -1,11 +1,12 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.utils import get_offset, verify_series
 
 
 def rma(
-    close: Series, length: int = None,
-    offset: int = None, **kwargs
+    close: Series, length: Int = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """wildeR's Moving Average (RMA)
 

pandas_ta/overlap/sinwma.py

@@ -1,12 +1,13 @@
 # -*- coding: utf-8 -*-
 from numpy import pi, sin
 from pandas import Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.utils import get_offset, verify_series, weights
 
 
 def sinwma(
-    close: Series, length: int = None,
-    offset: int = None, **kwargs
+    close: Series, length: Int = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Sine Weighted Moving Average (SWMA)
 

pandas_ta/overlap/sma.py

@@ -1,6 +1,7 @@
 # -*- coding: utf-8 -*-
 from numpy import convolve, ndarray, ones
 from pandas import Series
+from pandas_ta._typing import Array, DictLike, Int
 from pandas_ta.maps import Imports
 from pandas_ta.utils import get_offset, np_prepend, verify_series
 
@@ -12,7 +13,7 @@ except ImportError:
 
 
 @njit
-def np_sma(x: ndarray, n: int):
+def np_sma(x: Array, n: Int):
     """https://github.com/numba/numba/issues/4119"""
     result = convolve(ones(n) / n, x)[n - 1:1 - n]
     return np_prepend(result, n - 1)
@@ -32,9 +33,8 @@ def np_sma(x: ndarray, n: int):
 
 
 def sma(
-    close: Series, length: int = None,
-    talib: bool = None,
-    offset: int = None, **kwargs
+    close: Series, length: Int = None, talib: bool = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Simple Moving Average (SMA)
 

pandas_ta/overlap/smma.py

@@ -1,14 +1,15 @@
 # -*- coding: utf-8 -*-
 from numpy import nan
 from pandas import Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.ma import ma
 from pandas_ta.utils import get_offset, verify_series
 
 
 def smma(
-    close: Series, length: int = None,
+    close: Series, length: Int = None,
     mamode: str = None, talib: bool = None,
-    offset: int = None, **kwargs
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """SMoothed Moving Average (SMMA)
 

pandas_ta/overlap/ssf.py

@@ -1,6 +1,7 @@
 # -*- coding: utf-8 -*-
-from numpy import copy, cos, exp, ndarray
+from numpy import copy, cos, exp
 from pandas import Series
+from pandas_ta._typing import Array, DictLike, Int, IntFloat
 from pandas_ta.utils import get_offset, verify_series
 
 
@@ -11,7 +12,7 @@ except ImportError:
 
 
 @njit
-def np_ssf(x: ndarray, n: int, pi: float, sqrt2: float):
+def np_ssf(x: Array, n: Int, pi: IntFloat, sqrt2: IntFloat):
     """Ehler's Super Smoother Filter
     http://traders.com/documentation/feedbk_docs/2014/01/traderstips.html
     """
@@ -28,7 +29,7 @@ def np_ssf(x: ndarray, n: int, pi: float, sqrt2: float):
 
 
 @njit
-def np_ssf_everget(x: ndarray, n: int, pi: float, sqrt2: float):
+def np_ssf_everget(x: Array, n: Int, pi: IntFloat, sqrt2: IntFloat):
     """John F. Ehler's Super Smoother Filter by Everget (2 poles), Tradingview
     https://www.tradingview.com/script/VdJy0yBJ-Ehlers-Super-Smoother-Filter/
     """
@@ -44,9 +45,9 @@ def np_ssf_everget(x: ndarray, n: int, pi: float, sqrt2: float):
 
 
 def ssf(
-    close: Series, length: int = None,
-    everget: bool = None, pi: float = None, sqrt2: float = None,
-    offset: int = None, **kwargs
+    close: Series, length: Int = None,
+    everget: bool = None, pi: IntFloat = None, sqrt2: IntFloat = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Ehler's Super Smoother Filter (SSF) © 2013
 

pandas_ta/overlap/ssf3.py

@@ -1,6 +1,7 @@
 # -*- coding: utf-8 -*-
-from numpy import copy, cos, exp, ndarray
+from numpy import copy, cos, exp
 from pandas import Series
+from pandas_ta._typing import Array, DictLike, Int, IntFloat
 from pandas_ta.utils import get_offset, verify_series
 
 try:
@@ -10,7 +11,7 @@ except ImportError:
 
 
 @njit
-def np_ssf3(x: ndarray, n: int, pi: float, sqrt3: float):
+def np_ssf3(x: Array, n: Int, pi: IntFloat, sqrt3: IntFloat):
     """John F. Ehler's Super Smoother Filter by Everget (3 poles), Tradingview
     https://www.tradingview.com/script/VdJy0yBJ-Ehlers-Super-Smoother-Filter/"""
     m, result = x.size, copy(x)
@@ -31,9 +32,9 @@ def np_ssf3(x: ndarray, n: int, pi: float, sqrt3: float):
 
 
 def ssf3(
-    close: Series, length: int = None,
-    pi: float = None, sqrt3: float = None,
-    offset=None, **kwargs
+    close: Series, length: Int = None,
+    pi: IntFloat = None, sqrt3: IntFloat = None,
+    offset: Int = None, **kwargs: DictLike
 ):
     """Ehler's 3 Pole Super Smoother Filter (SSF) © 2013
 

pandas_ta/overlap/supertrend.py

@@ -1,6 +1,7 @@
 # -*- coding: utf-8 -*-
 from numpy import nan
 from pandas import DataFrame, Series
+from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.overlap import hl2
 from pandas_ta.utils import get_offset, verify_series
 from pandas_ta.volatility import atr
@@ -8,8 +9,8 @@ from pandas_ta.volatility import atr
 
 def supertrend(
     high: Series, low: Series, close: Series,
-    length: int = None, multiplier: float = None,
-    offset: int = None, **kwargs
+    length: Int = None, multiplier: IntFloat = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> DataFrame:
     """Supertrend (supertrend)
 

pandas_ta/overlap/swma.py

@@ -1,11 +1,12 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.utils import get_offset, symmetric_triangle, verify_series, weights
 
 
 def swma(
-    close: Series, length: int = None, asc: bool = None,
-    offset: int = None, **kwargs
+    close: Series, length: Int = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Symmetric Weighted Moving Average (SWMA)
 
@@ -20,7 +21,6 @@ def swma(
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 10
-        asc (bool): Recent values weigh more. Default: True
         offset (int): How many periods to offset the result. Default: 0
 
     Kwargs:

pandas_ta/overlap/t3.py

@@ -1,13 +1,14 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
+from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.maps import Imports
 from pandas_ta.utils import get_offset, verify_series
 from .ema import ema
 
 
 def t3(
-    close: Series, length: int = None, a: float = None, talib: bool = None,
-    offset: int = None, **kwargs
+    close: Series, length: Int = None, a: IntFloat = None, talib: bool = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Tim Tillson's T3 Moving Average (T3)
 

pandas_ta/overlap/tema.py

@@ -1,13 +1,14 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.maps import Imports
 from pandas_ta.utils import get_offset, verify_series
 from .ema import ema
 
 
 def tema(
-    close: Series, length: int = None, talib: bool = None,
-    offset: int = None, **kwargs
+    close: Series, length: Int = None, talib: bool = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Triple Exponential Moving Average (TEMA)
 

pandas_ta/overlap/trima.py

@@ -1,13 +1,14 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.maps import Imports
 from pandas_ta.utils import get_offset, verify_series
 from .sma import sma
 
 
 def trima(
-    close: Series, length: int = None, talib: bool = None,
-    offset: int = None, **kwargs
+    close: Series, length: Int = None, talib: bool = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Triangular Moving Average (TRIMA)
 

pandas_ta/overlap/vidya.py

@@ -1,12 +1,13 @@
 # -*- coding: utf-8 -*-
 from numpy import nan
 from pandas import Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.utils import get_drift, get_offset, verify_series
 
 
 def vidya(
-    close: Series, length: int = None, drift: int = None,
-    offset: int = None, **kwargs
+    close: Series, length: Int = None, drift: Int = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Variable Index Dynamic Average (VIDYA)
 

pandas_ta/overlap/vwap.py

@@ -1,13 +1,14 @@
 # -*- coding: utf-8 -*-
 from pandas import DataFrame, Series
+from pandas_ta._typing import DictLike, Int, List
 from pandas_ta.overlap import hlc3
 from pandas_ta.utils import get_offset, is_datetime_ordered, verify_series
 
 
 def vwap(
     high: Series, low: Series, close: Series, volume: Series,
-    anchor: str = None, bands: list = None,
-    offset: int = None, **kwargs
+    anchor: str = None, bands: List = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Volume Weighted Average Price (VWAP)
 

pandas_ta/overlap/vwma.py

@@ -1,12 +1,13 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.overlap import sma
 from pandas_ta.utils import get_offset, verify_series
 
 
 def vwma(
-    close: Series, volume: Series, length: int = None,
-    offset: int = None, **kwargs
+    close: Series, volume: Series, length: Int = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Volume Weighted Moving Average (VWMA)
 

pandas_ta/overlap/wcp.py

@@ -1,12 +1,13 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.maps import Imports
 from pandas_ta.utils import get_offset, verify_series
 
 
 def wcp(
     high: Series, low: Series, close: Series, talib: bool = None,
-    offset: int = None, **kwargs
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Weighted Closing Price (WCP)
 

pandas_ta/overlap/wma.py

@@ -1,14 +1,15 @@
 # -*- coding: utf-8 -*-
 from numpy import arange, dot
 from pandas import Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.maps import Imports
 from pandas_ta.utils import get_offset, verify_series
 
 
 def wma(
-    close: Series, length: int = None,
+    close: Series, length: Int = None,
     asc: bool = None, talib: bool = None,
-    offset: int = None, **kwargs
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Weighted Moving Average (WMA)
 

pandas_ta/overlap/zlma.py

@@ -1,5 +1,6 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.utils import get_offset, verify_series
 from .dema import dema
 from .ema import ema
@@ -22,7 +23,7 @@ from .wma import wma
 
 # Not ideal but it works. Submit a PR for a better solution. =)
 # This design pattern is undesirable
-def _ma(mamode, **kwargs):
+def _ma(mamode: str, **kwargs: DictLike):
     if mamode == "dema":
         return dema(**kwargs)
     elif mamode == "fwma":
@@ -58,9 +59,10 @@ def _ma(mamode, **kwargs):
     else:
         return ema(**kwargs)
 
+
 def zlma(
-    close: Series, length: int = None, mamode: str = None,
-    offset: int = None, **kwargs
+    close: Series, length: Int = None, mamode: str = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Zero Lag Moving Average (ZLMA)
 

pandas_ta/performance/drawdown.py

@@ -1,11 +1,12 @@
 # -*- coding: utf-8 -*-
 from numpy import log, seterr
 from pandas import DataFrame, Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.utils import get_offset, verify_series
 
 
 def drawdown(
-    close: Series, offset: int = None, **kwargs
+    close: Series, offset: Int = None, **kwargs: DictLike
 ) -> DataFrame:
     """Drawdown (DD)
 

pandas_ta/performance/log_return.py

@@ -1,12 +1,13 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
 from numpy import log, nan, roll
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.utils import get_offset, verify_series
 
 
 def log_return(
-    close: Series, length: int = None, cumulative: bool = None,
-    offset: int = None, **kwargs
+    close: Series, length: Int = None, cumulative: bool = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Log Return
 

pandas_ta/performance/percent_return.py

@@ -1,12 +1,13 @@
 # -*- coding: utf-8 -*-
 from numpy import nan, roll
 from pandas import Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.utils import get_offset, verify_series
 
 
 def percent_return(
-    close: Series, length: int = None, cumulative: bool = None,
-    offset: int = None, **kwargs
+    close: Series, length: Int = None, cumulative: bool = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Percent Return
 

pandas_ta/statistics/entropy.py

@@ -1,12 +1,13 @@
 # -*- coding: utf-8 -*-
 from numpy import log
 from pandas import Series
+from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.utils import get_offset, verify_series
 
 
 def entropy(
-    close: Series, length: int = None, base: float = None,
-    offset: int = None, **kwargs
+    close: Series, length: Int = None, base: IntFloat = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Entropy (ENTP)
 

pandas_ta/statistics/kurtosis.py

@@ -1,11 +1,12 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.utils import get_offset, verify_series
 
 
 def kurtosis(
-    close: Series, length: int = None,
-    offset: int = None, **kwargs
+    close: Series, length: Int = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Rolling Kurtosis
 

pandas_ta/statistics/mad.py

@@ -1,12 +1,13 @@
 # -*- coding: utf-8 -*-
 from numpy import fabs
 from pandas import Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.utils import get_offset, verify_series
 
 
 def mad(
-    close: Series, length: int = None,
-    offset: int = None, **kwargs
+    close: Series, length: Int = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Rolling Mean Absolute Deviation
 

pandas_ta/statistics/median.py

@@ -1,11 +1,12 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
+from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.utils import get_offset, verify_series
 
 
 def median(
-    close: Series, length: int = None,
-    offset: int = None, **kwargs
+    close: Series, length: Int = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Rolling Median
 

pandas_ta/statistics/quantile.py

@@ -1,11 +1,12 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
+from pandas_ta._typing import DictLike, Int, IntFloat
 from pandas_ta.utils import get_offset, verify_series
 
 
 def quantile(
-    close: Series, length: int = None, q: float = None,
-    offset: int = None, **kwargs
+    close: Series, length: Int = None, q: IntFloat = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Rolling Quantile
 

pandas_ta/statistics/skew.py

@@ -1,11 +1,12 @@
 # -*- coding: utf-8 -*-
 from pandas import Series
+from pandas_ta._typing import DictLike, Int
 from pandas_ta.utils import get_offset, verify_series
 
 
 def skew(
-    close: Series, length: int = None,
-    offset: int = None, **kwargs
+    close: Series, length: Int = None,
+    offset: Int = None, **kwargs: DictLike
 ) -> Series:
     """Rolling Skew